Literature DB >> 22589215

Combining many interaction networks to predict gene function and analyze gene lists.

Sara Mostafavi1, Quaid Morris.   

Abstract

In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems.
© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mesh:

Substances:

Year:  2012        PMID: 22589215     DOI: 10.1002/pmic.201100607

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  24 in total

Review 1.  Methods for biological data integration: perspectives and challenges.

Authors:  Vladimir Gligorijević; Nataša Pržulj
Journal:  J R Soc Interface       Date:  2015-11-06       Impact factor: 4.118

2.  Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks.

Authors:  Mark Grimes; Benjamin Hall; Lauren Foltz; Tyler Levy; Klarisa Rikova; Jeremiah Gaiser; William Cook; Ekaterina Smirnova; Travis Wheeler; Neil R Clark; Alexander Lachmann; Bin Zhang; Peter Hornbeck; Avi Ma'ayan; Michael Comb
Journal:  Sci Signal       Date:  2018-05-22       Impact factor: 8.192

3.  SIFTER search: a web server for accurate phylogeny-based protein function prediction.

Authors:  Sayed M Sahraeian; Kevin R Luo; Steven E Brenner
Journal:  Nucleic Acids Res       Date:  2015-05-15       Impact factor: 16.971

4.  Machine-learning techniques for the prediction of protein-protein interactions.

Authors:  Debasree Sarkar; Sudipto Saha
Journal:  J Biosci       Date:  2019-09       Impact factor: 1.826

5.  DNA methylation modifies urine biomarker levels in 1,6-hexamethylene diisocyanate exposed workers: a pilot study.

Authors:  Leena A Nylander-French; Michael C Wu; John E French; Jayne C Boyer; Lisa Smeester; Alison P Sanders; Rebecca C Fry
Journal:  Toxicol Lett       Date:  2014-10-22       Impact factor: 4.372

6.  Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold.

Authors:  Marinka Zitnik; Blaž Zupan
Journal:  Pac Symp Biocomput       Date:  2014

7.  Modular biological function is most effectively captured by combining molecular interaction data types.

Authors:  Ryan M Ames; Jamie I Macpherson; John W Pinney; Simon C Lovell; David L Robertson
Journal:  PLoS One       Date:  2013-05-03       Impact factor: 3.240

8.  FFPred 2.0: improved homology-independent prediction of gene ontology terms for eukaryotic protein sequences.

Authors:  Federico Minneci; Damiano Piovesan; Domenico Cozzetto; David T Jones
Journal:  PLoS One       Date:  2013-05-22       Impact factor: 3.240

9.  GeneMANIA prediction server 2013 update.

Authors:  Khalid Zuberi; Max Franz; Harold Rodriguez; Jason Montojo; Christian Tannus Lopes; Gary D Bader; Quaid Morris
Journal:  Nucleic Acids Res       Date:  2013-07       Impact factor: 16.971

10.  Wrangling phosphoproteomic data to elucidate cancer signaling pathways.

Authors:  Mark L Grimes; Wan-Jui Lee; Laurens van der Maaten; Paul Shannon
Journal:  PLoS One       Date:  2013-01-03       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.